About Course
Master the full ML lifecycle – from model versioning and pipeline automation to deployment, monitoring, and drift detection in production.
What you’ll learn:
– ML model versioning & registry
– Pipeline automation & feature stores
– Model deployment & monitoring
– Drift detection & retraining
– Production-grade MLOps practices
Career outcomes:
MLOps Engineer, ML Platform Engineer, AI/ML DevOps Specialist, Data Engineering Roles
Student Ratings & Reviews
No Review Yet
